Improvement of Periodic Limb Movements following Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND/AIMS: Periodic limb movements are common in patients with end-stage renal disease. Kidney transplantation significantly improves renal function and may therefore reduce periodic limb movements. We evaluated the effect of kidney transplantation on periodic limb movements in a group of patients with end-stage renal disease. METHODS: Eighteen patients (aged 27-65) who were receiving dialysis and were candidates for living donor or deceased donor kidney transplantation (n = 12) or were predialysis with a suitable living donor arranged (n = 6) were recruited from the transplant clinic. Attended overnight polysomnography was performed before and after kidney transplantation. Patients were divided based on a periodic limb movement index >15 events/h during sleep. RESULTS: Kidney transplantation was associated with a significant reduction in periodic limb movement index in all patients (8 (0-110) events/h vs. 2 (0-80) events/h) and this reduction was greatest in 7 patients with a periodic limb movement index >15 events/h (40 (24-110) events/h to 14 (1-80) events/h, p < 0.005). CONCLUSION: Successful kidney transplantation improves periodic limb movements in patients with end-stage renal disease. This may improve sleep quality and sleep-related quality of life in kidney transplant recipients, which should have a beneficial impact on clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".